Why is Data Management important? Discover the benefits your business can enjoy

data management
By , Data Platform Engineer

Data is the fuel that powers your business. It allows you to make more informed decisions, track customer engagement, and analyze performance. That’s just for starters because there are many more valuable benefits that can be realized. 

However, if your data isn’t managed properly, it can quickly become messy and unreliable, which can lead you to making inaccurate conclusions and poor decisions. Therefore, it’s necessary to adapt your data management to the relevant needs of your organization, which will help improve the clarity and efficiency of all of your data-related processes and procedures.

In the article below, we’ll explore what data management is, how it’s classified and how it can help your business succeed, giving you a strategic advantage among your competitors in the market.

What is Data Management?

Data management is the process of collecting, organizing, manipulating, storing and securing data to enable better decision-making, improved efficiency, reduced costs, enhanced customer experiences along with many other benefits. To ensure success in today’s competitive market, businesses should make their data correspond to their unique business requirements. Usually, this can be achieved by  keeping the information in their data management system:

  • Accessible

  • Understandable

  • Secure

  • Compliant with all Laws and Regulations

  • Optimized for Automation and Analytics

  • Adaptable to Changing Technologies

  • Cost-Effective

The software we utilize at Solvd  assists with all the processes from data preparation, to cataloging, search, and governance, helping businesses to manage their information according to their individual needs.

If companies wish to be successful in this domain, it’s important for all stakeholders involved (executives, managers and employees) to be aware of and understand the value of high-quality data management and the participation of all involved in its  procedures and processes.

Types of Data Management

Data Management can take many different forms. The most widely used forms are listed below:

  • Operational Data Management involves capturing, validating and managing data in a system. It’s most commonly used for transaction processing and reporting.

  • Analytical Data Management includes the acquisition, integration, cleaning and storage of large amounts of data from different sources. The focus here is about analyzing the stored information to make more informed (better) decisions.

  • Master Data Management (MDM) is a system for managing and storing all the master data of an organization in one single place. Its aim is to create a unified view of the information from multiple sources.

  • Cloud Data Management is the storing and managing of data on cloud-based platforms like AWS, Azure or Google Cloud Platform. It allows companies to access their data anywhere and anytime with improved scalability and security.

  • Big Data Management focuses on dealing with huge amounts of structured, semi-structured and unstructured datasets to generate insights for better decision-making. Technologies such as Hadoop and MapReduce are often used for this purpose.

  • Privacy Data Management focuses on monitoring and securing the privacy of customers' personal information. It includes implementations like anonymization, pseudonymization and tokenization.

  • Dynamic Data Management is a system for managing streaming data across different sources in real time. It enables organizations to track changes in customer behavior and market trends quickly.

  • Governance Data Management involves creating policies and procedures to ensure compliance with laws and regulations related to customer data usage. Its aim is to protect the rights of customers while still allowing organizations to use their data for business purposes.

  • Blockchain Data Management focuses on using distributed ledger technology to manage and secure digital records. It ensures that all transactions are transparent and trustworthy.

  • Metadata Management involves creating a comprehensive inventory of the different types of data stored in an organization along with their relationships with each other. Its goal is to create a unified view of the data for better analysis and decision making.

  • Data Quality Management (DQM) ensures the accuracy, completeness, consistency and integrity of the data stored in an organization through processes such as validation and verification.

  • Mobile Data Management involves managing mobile device usage within an organization. It includes features like access control, app usage tracking, data encryption and remote wiping of lost devices.

  • Information Lifecycle Management (ILM) is responsible for managing the entire life-cycle of information within an organization, including its creation, storage, usage and disposal. Its main purpose is to ensure the efficient usage of resources while being compliant with all laws and regulations.

  • Structured Query Language (SQL) Data Management involves using a special programming language to inquire related databases for retrieving or modifying information. 

  • In-Memory Data Management is oriented toward storing and processing large volumes of data from memory instead of disk drives to speed up operations. It’s most often used in applications like online gaming, analytics and streaming.

  • NoSQL Data Management focuses on using non-relational databases for storing and managing large amounts of data without imposing any strict schema or structure. It is often used for applications such as web development, real-time analytics and content management systems.

  • Distributed Data Management involves storing and managing data across multiple servers that are located in different geographical locations. Its aim is to improve the scalability, efficiency and security of the overall system.

Companies can utilize any or all of the above-mentioned types of data management depending on their user requirements and the specifics of the project. In order to ensure the best possible results, companies should thoroughly explore all the relevant options available before making their final choices.

Why is Data Management important to your business?

The effective use of data management brings many benefits to businesses that have decided to implement it. 

  • Improved Efficiency. By organizing and managing large amounts of data, businesses can easily access the necessary information whenever they need it. Such an approach improves workflow and minimizes wasted time spent by searching through files or databases manually.

  • Increased Accuracy. Automated systems for managing data help reduce the errors created by human mistakes that could have led to inaccurate results and conclusions. 

  • Improved Security. Thanks to the implementation of effective security measures, companies can protect their sensitive information from unauthorized access and subsequent unlawful usage.

  • Reduced Costs. Automation and optimization of processes (data entry and/or analysis) help businesses save money overall. Improved data storage strategies can also reduce the need for costly hardware upgrades and/or the use of additional unnecessary storage space.

  • Enhanced Customer Experience. By collecting and analyzing customer data, organizations can gain a better understanding of their user’s needs, preferences and behaviors. Such an approach helps them develop more effective, targeted marketing campaigns that are tailored specifically to their customers’ needs and interests.

  • Better Decision Making. With accurate, up-to-date data right at their fingertips, businesses can make decisions quickly and confidently based on reliable evidence instead of just by gut feeling and guesswork.

To sum it all up, thanks to leveraging the power of data, businesses can discover and unlock their true performance potential and benefit from all of the positive results gained.

data management benefits

The challenges involved with Data Management

Contemporary organizations face a lot of challenges while managing the data within their strategies and campaigns. 

  • Quality Control. Ensuring the highest level of data quality and accuracy is essential for reliable analysis and decision-making. Poorly managed data can lead to costly mistakes, so it’s important to have mechanisms in place to ensure the integrity of data over time.

  • Storage and Retrieval. Keeping track of vast amounts of data can be tricky, especially as technology changes and new formats emerge. Organizations must develop systems that will allow them to easily store, retrieve, and update their data quickly and efficiently when needed.

  • Security. Data breaches are a constant threat in today's digital landscape, making security an extremely important aspect of a company's business strategy. Organizations must ensure that their data is protected from external threats and can be recovered quickly if breached.

  • Governance. Developing policies and procedures to support data sharing, access, and authorization is essential for maintaining control over digital assets. Without adequate governance, organizations risk losing sight of their data along with poor decision making that’s based on inadequate or incomplete information.

  • Compliance. With increasingly stricter government regulations about the handling of personal data, ensuring compliance with relevant laws and regulations becomes a critical part of any organization’s data management strategy. Companies must develop strategies to stay compliant without compromising the security or privacy of users’ data.

  • Analytics. Data analysis has become an integral part of decision-making for many organizations. However, it can be challenging to unlock the vital  information and real-time metrics regarding the project. Thus, companies need to use advanced tools and implement the right techniques to effectively understand data patterns and trends. 

  • Automation. As data volumes continue to grow exponentially, automation can be a useful tool for streamlining processes and improving the accuracy of results. Organizations must consider investing in automated solutions that can extract meaningful insights from large datasets quickly, accurately and cost-effectively.

  • Adaptability. As technology advances, organizations must ensure that their systems and processes remain adaptable to changing protocols, standards, and technologies. This may require deploying AI or machine learning algorithms to evolve the organization's data strategies over time.

  • User Experience. End-users should be taken into account when developing data management strategies. Organizations must strive to design interfaces that are intuitive and easy to use by non-technical users, enabling them to quickly and easily access the data they need without having to understand complex technical concepts or jargon.

  • Sustainability. Ensuring the right balance between maintaining existing databases and adding new ones is key to reaching sustainability. Companies should regularly review their current databases to identify areas for improvement and take into consideration the long-term costs associated with managing ever-growing datasets.

  • Collaboration. When multiple stakeholders are involved in a project, it’s important to ensure that everyone is on the same page when it comes to data management. Organizations must develop systems and protocols that facilitate collaboration between different teams and/or divisions, enabling them to access shared resources easily.

  • Cost Management. Keeping track of all the expenses related to data can be difficult, but it’s essential for staying on budget and maximizing return on investment (ROI). Companies should strive to develop cost-efficient solutions that can reduce overhead and optimize resource utilization.

By understanding the above-mentioned challenges, organizations can develop robust strategies for managing their data resources more efficiently. Doing so will enable them to maximize the value of their digital assets and stay ahead of the competition in today’s fast-paced and competitive digital world.

data management challenges

In conclusion

Data management is a crucial part of any business, as it helps companies ensure that they are making the best possible decisions and protecting themselves against fraud while staying compliant with all laws and regulations. It’s especially important, in today’s times when data is created and consumed at unprecedented rates. 

The secret to harnessing the potential of data as a force for organizational success is to have a thorough understanding of how this domain functions and how businesses can implement the most effective policies and procedures to get the most out of it.

Remember, your business data system can become a “gold mine”. But, to get the “gold” (important information) out of the mine, you’ll need to hire data management experts that use the right tools. Consider hiring us at Solvd so you can get the most out of  your next digital project.

Author Franco Scagnetti
Data Platform Engineer
Franco Scagnetti graduated from National Technological University (Buenos Aires, Argentina Regional Faculty) with a Bachelor's degree in Systems Engineering. He’s been helping companies to turn data into valuable insights for over seven years now.

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